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Forecasting the industrial production using alternative factor models and business survey data

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  • Mauro Costantini

Abstract

This paper compares the forecasting performance of three alternative factor models based on business survey data for the industrial production in Italy. The first model uses static principal component analysis, while the other two apply dynamic principal component analysis in frequency domain and subspace algorithms for state-space representation, respectively. Once the factors are extracted from the business survey data, then they are included into a single equation to predict the industrial production index. The forecast results show that the three factor models have a better performance than that of a simple autoregressive benchmark model regardless of the specification and estimation methods. Furthermore, the state-space model yields superior forecasts amongst the factor models.

Suggested Citation

  • Mauro Costantini, 2013. "Forecasting the industrial production using alternative factor models and business survey data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(10), pages 2275-2289, October.
    • Handle: RePEc:taf:japsta:v:40:y:2013:i:10:p:2275-2289
    DOI: 10.1080/02664763.2013.809870
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    Cited by:

    1. Galdi, Giulio & Casarin, Roberto & Ferrari, Davide & Fezzi, Carlo & Ravazzolo, Francesco, 2023. "Nowcasting industrial production using linear and non-linear models of electricity demand," Energy Economics, Elsevier, vol. 126(C).
    2. Hacievliyagil Nuri & Eksi Ibrahim Halil, 2019. "A Micro Based Study on Bank Credit and Economic Growth: Manufacturing Sub-Sectors Analysis," South East European Journal of Economics and Business, Sciendo, vol. 14(1), pages 72-91, June.
    3. Piotr Białowolski, 2015. "Concepts of Confidence in Tendency Survey Research: An Assessment with Multi-group Confirmatory Factor Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 123(1), pages 281-302, August.
    4. Riccardo Corradini, 2019. "A Set of State–Space Models at a High Disaggregation Level to Forecast Italian Industrial Production," J, MDPI, vol. 2(4), pages 1-53, November.

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